The model architecture we study is an LSTM-based multi-task model for IC and NER tasks, where we use 300-dimension fastText word embeddings [1]}, trained on a large voice assistant corpus.The text corpus contains data transcribed by an automatic speech recognition system. A shared 256-dimension Bi-LSTM encoder and two separate task-specific Bi-LSTM encoders (256-dimension) are applied to encode the sentences. A softmax layer and a conditional random field (CRF) layer are used to produce predictions for IC and NER, respectively.
